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Bayesian Inference

Bayesian Inference is a methodology that employs Bayes Rule to estimate parameters (and their full posterior).

Papers

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Showing 1681–1690 of 2226 papers

TitleStatusHype
A Stochastic Robust Adaptive Systems Level Approach to Stabilizing Large-Scale Uncertain Markovian Jump Linear Systems—0
A stochastic version of Stein Variational Gradient Descent for efficient sampling—0
A Survey of Uncertainty Estimation in LLMs: Theory Meets Practice—0
A survey on Bayesian inference for Gaussian mixture model—0
A Survey on Blood Pressure Measurement Technologies: Addressing Potential Sources of Bias—0
A Symbolic and Statistical Learning Framework to Discover Bioprocessing Regulatory Mechanism: Cell Culture Example—0
Asymptotic Bayesian Generalization Error in Latent Dirichlet Allocation and Stochastic Matrix Factorization—0
Asymptotic properties of Bayesian inference in linear regression with a structural break—0
A Technical Critique of Some Parts of the Free Energy Principle—0
A theory of data variability in Neural Network Bayesian inference—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1F-SWAAccuracy83.61—Unverified
2F-SWAGAccuracy80.93—Unverified